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All Journal Jupiter Jurnal Media Infotama Syntax Jurnal Informatika Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) Journal of Animation & Games Studies Format : Jurnal Imiah Teknik Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer INTEGER: Journal of Information Technology JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Antivirus : Jurnal Ilmiah Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Journal of Appropriate Technology for Community Services ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Ihsan: Jurnal Pengabdian Masyarakat Jifosi Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Nasional Pengabdian Masyarakat International Journal of Data Science, Engineering, and Analytics (IJDASEA) Unram Journal of Community Service (UJCS) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Minfo Polgan (JMP) Literasi Nusantara Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Informatika Polinema (JIP) Repeater: Publikasi Teknik Informatika dan Jaringan International Journal of Information Engineering and Science Jurnal Sistem Informasi dan Ilmu Komputer Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Fetty Tri Anggraeny
Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294 Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Implementasi Metode CNN Menggunakan Arsitektur Resnet101 Pada Citra Penyakit Daun Padi Putri, Shintyadhita Wirawan; Azira, Volem Alvaro; Siregar, Talitha Aurora Nadenggan; Salsabilah, Rafani Bardatus; Anggraeny, Fetty Tri
Jurnal Ilmiah Teknologi Informasi dan Robotika Vol. 7 No. 1 (2025): Jurnal Ilmiah Teknologi Informasi dan Robotika
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifti.v7i1.157

Abstract

Indonesia sebagai negara agraris memiliki peran penting dalam sektor pertanian terhadap perekonomian negara. Salah satu komoditas utama yang menjadi tulang punggung sektor ini adalah padi. Namun, produktivitas tanaman padi seringkali terganggu akibat penyakit seperti bacterial leaf blight, brown spot, dan leaf smut yang menyerang daun dari tanaman tersebut. Model yang dikembangkan, kemudian diuji menggunakan beberapa metrik seperti akurasi, precision, recall, dan f1-score yang berfungsi sebagai alat ukur performa model. Tujuan dari penelitian ini adalah pengembangan sistem klasifikasi penyakit daun padi menggunakan metode CNN dengan model ResNet101. Hasil penelitian menunjukkan bahwa model ResNet101 dapat mengenali penyakit pada daun padi dengan nilai akurasi sebesar 33% pada data validasi yang masih harus ditingkatkan kembali pada beberapa kelasnya. Harapan dari penelitian ini adalah dapat mendiagnosis penyakit tanaman padi secara otomatis, yang diharapkan dapat mempercepat deteksi dan penanggulangan penyakit pada tanaman padi. Pengembangan riset berikutnya dapat diarahkan pada optimalisasi keseimbangan data serta pemanfaatan teknik penanganan overfitting yang lebih variatif.
Model Hibrida Deret Waktu Adaptif untuk Peramalan Dinamis Permintaan Penumpang Kereta Api Menggunakan Kalman Filter Isworo, Muhamad Raihan Ramadhani; Tri Anggraeny, Fetty; Junaidi, Achmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Peramalan permintaan penumpang kereta api yang akurat sangat penting untuk optimasi operasional dan perencanaan layanan transportasi PT KAI. Model peramalan konvensional sering menghadapi tantangan dalam menangani dinamika permintaan yang kompleks, terutama saat terjadi perubahan pola mendadak dan kejadian khusus seperti hari libur atau kejadian luar biasa. Penelitian ini mengusulkan model hibrida adaptif yang menggabungkan SARIMAX dan Prophet dengan optimasi bobot menggunakan Kalman Filter. Data yang digunakan adalah jumlah penumpang bulanan PT KAI DAOP 8 periode 2016-2023 yang mencakup fluktuasi musiman dan kejadian khusus yang mempengaruhi permintaan penumpang. Hasil menunjukkan bahwa model hibrida adaptif mencapai MAPE 4.32%, lebih baik dibandingkan dengan SARIMAX (7.72%) dan Prophet (6.06%). Kalman Filter berhasil mengoptimalkan bobot secara dinamis, meningkatkan kemampuan adaptasi model terhadap perubahan pola permintaan. Model ini menunjukkan performa akurasi dan stabilitas yang tinggi, serta dapat digunakan untuk meramalkan permintaan penumpang di masa depan dan memberikan rekomendasi untuk perencanaan kapasitas PT KAI yang lebih efektif.   Abstract Accurate forecasting of rail passenger demand is essential for operational optimization and planning of transportation services. Conventional forecasting models often face challenges in handling complex demand dynamics, especially when sudden pattern changes and special events occur. This study proposes an adaptive hybrid model combining SARIMAX and Prophet with weight optimization using the Kalman Filter. The data used is the monthly passenger number of PT KAI DAOP 8 from 2016 to 2023, which includes seasonal fluctuations and special events affecting passenger demand. The results show that the adaptive hybrid model achieved a MAPE of 4.32%, better than SARIMAX (7.72%) and Prophet (6.06%). The Kalman Filter successfully optimized the weights dynamically, improving the model's adaptability to changing demand patterns. This model demonstrates high accuracy and stability, and can be used to forecast future passenger demand and provide recommendations for more effective capacity planning for PT KAI.
Prophet–LightGBM Hybrid Model Implementation in Cafe Menu Sales Prediction: Implementasi Model Hybrid Prophet–LightGBM dalam Prediksi Penjualan Menu Kafe Erik evranata Pardede; Fetty Tri Anggraeny; Achmad Junaidi
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to improve the accuracy of sales forecasting for cafe menu items through the development of a hybrid model that combines the Facebook Prophet and LightGBM algorithms. This hybrid model is designed to leverage the strengths of Prophet in detecting seasonal patterns and trends, as well as the ability of LightGBM to learn from residuals that are not captured by Prophet. The dataset used is sourced from Kaggle, containing cafe menu sales data, which includes information about the menu items, the quantity sold, and the transaction dates. Model evaluation was conducted using MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error) metrics. According to the results, the hybrid model shows significant improvement in forecasting accuracy, with MAPE of 5.83% for one menu item (cake), MAE of 0.84, and RMSE of 0.99, indicating better accuracy compared to the single models. This study is expected to provide valuable contributions to more efficient stock management and the development of more targeted marketing strategies for the cafe industry.
Kidney Stone Disease Diagnosis Using Shifted-Windows Transformer (SWIN Transformer): Diagnosis Penyakit Batu Ginjal Menggunakan Shifted-Windows Transformer (SWIN Transformer) Alfian Bima Prastyo; Fetty Tri Anggraeny; Retno Mumpuni
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kidney stones are a prevalent urological condition that, if undiagnosed that can lead to serious complications. Traditional diagnostic methods, such as manual ultrasound interpretation, are error-prone and time consuming, especially in areas with limited access to healthcare professionals. This research proposes the use of the Shifted Windows Transformer (Swin Transformer), a state-of-the-art deep learning model, to improve the classification of kidney stones in ultrasound images. The model is trained on a dataset of 9,396 kidney ultrasound images, categorized into two classes normal kidneys and kidneys with stones, sourced from a publicly available on Mendeley data Kidney dataset. The results demonstrate that the Swin Transformer achieves an impressive accuracy of 99.57%, surpassing others models like Convolutional Neural Networks (CNN) and Vision Transformers (ViT) by efficiently capturing both local and global features in high-resolution images. Practical implications include faster, more accurate diagnoses, particularly in regions lacking specialized radiologists. However, limitations of this model include its dependence on high-quality ultrasound images, which may not always be available in less-resourced settings. Additionally, the model’s performance may vary depending on the diversity of the dataset, limiting its generalizability in certain clinical environments. The need for substantial computational resources may also restrict the model's applicability in some healthcare settings. Despite these limitations, the Swin Transformer shows great promise as an automated tool for kidney stone detection, offering potential solutions for early diagnosis in remote and underdeveloped areas.
Perbandingan Algoritma Random Forest dan Logistic Regression Untuk Analisis Sentimen Ulasan Aplikasi Tumbuh Kembang Anak Di Play Store Muhammad Alfyando; Fetty Tri Anggraeny; Andreas Nugroho Sihananto
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 1 (2024): Februari : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i1.2262

Abstract

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.
Co-Authors Abdul Aziz Naufal Farizqi Abu Musa, Hammam Bara Ach.Diki Prasetyo, Ach.Diki Prasetyo Achmad Junaidi Achmad Junaidi, Achmad Adyani, Adelia Putri Afina Lina Nurlaili Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agussalim Ahmad Sofian Aris S Akbar, Fawwaz Ali Akbar, Iqbal Imani Khoirul Alfian Bima Prastyo Alfiani, Fina Alibasyah, Fahmi Nugroho Alviriza Ramadhan, Muhammad Amalia, Nadhia Rizqy Anabella, Linda Happy Andreas Nugroho Sihananto Andreas Nugroho Sihananto Aprillian, Farrel Archamul Fajar Pratama Ariadi, Kuncoro Atmojo, Unggul Widi Ayu Puspita, Nabila Ayuningrum, Agnes Athalia Azira, Volem Alvaro Azizi, Abrar Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa Cahyas, Jerry Ramadhani Dafauzan Bilal Syaifulloh Dedin F. Rosida Dedin Finatsiyatull Rosida Dianto, Alfian Rachmad Dimara, Denis Lizard Sambawo Dimas Saputra Dita Atasa Diyasa, I Gede Susrama Mas Dyan Agustin Dzulqornain, Muhammad Rif'an Eka Fitria Wulandari Erik evranata Pardede Eva Yulia Puspaningrum Evi Suryaningsih Fadillah, Mochamad Nor Fahmi Anugrah Danendra Faisal Muttaqin Farkhan, Farkhan Faturrahman Rahardjo, Iqbal Raihan Firjatullah, Adika Firza Prima Aditiawan Fitriansyah, Muhammad Daffa Gideon Setya Budiwitjaksono Habibi, Faisal Wildan Hadi, I Putu Mahardika Cahyana Handono, Stevanus Frangky Handoyo Prasetyo Hartanti, Syafrida Maulina Hasan, Ferry Hasby Bik, Ahmad Hasya, Astrini Hadina Hatta, Heliza Rahmania Henni Endah Wahanani Hilal, Muhammad Hsya, Astrini Hadina I Gede Susrama Masdiyasa Intan Yuniar Purbasari Intan Yuniar Purbasari, Intan Yuniar Irawan, Nauval Maulana Rizky Isworo, Muhamad Raihan Ramadhani Julianto Dwi Putra, Rico Khairil Amin, Mohammad Khonsa Salsabila Kusuma, Nugraha Varrel Made Hanindia Prami Swari Mahardika Virgo Wuryantoro Manalu, Daniel Maulana, Hendra Maulana, Rafie Ishaq Meike Hardianti Merdin Risalul Abrori Mochamad Nor Fadillah Mohamad Ilham Prasetyo Raharjo Mohammad Idhom Monica Widiasri, Monica Muhammad Ahsanur Rafi Muhammad Alfin Jimly Asshiddiqie Muhammad Alfyando Muhammad Dawam Fakhri Muhammad Muharrom Al Haromainy Munoto Mustika Rizki, Agung Mutoffar, Muhamad Malik Naila, Amelia Maslaqun Nashrulloh, Muhammad Atay Nadhif Nicholas, Sandy Novarina, Fitria Nugroho Sihananto, Andreas Nur Aini Ersanti Nurfiana Panjaitan, Tompo Pradana, Marchel Adias Pradipta, M. Najmi Arya Prastya, Ade Fathoni Pratama Wirya Atmaja Pratama Wirya Atmaja Pratama, Muhammad Lutfi Pratiwi, Nisa Putra, Chrystia Aji Putra, Riza Satria Putri, Shintyadhita Wirawan Radical Rakhman Rafie Ishaq Maulana Rama Andika Jorgie Rangga Widiasmara Rayhan Rizal Mahendra Retno Mumpuni Retno Mumpuni Reza Aminullah Ridho Aji Pangestu Ronggo Alit Salsabilah, Rafani Bardatus Sandy Rizkyando Sani, Yusmia Washiatus Sanjaya, Alvian Dwi Sankalla, Sabda Saputra, Rendi Cahya Satria, Ramadhan Dani Satria, Vinza Hedi Septyono, Muhammad Bagas Setianto, Christian Wahyu Shalehuddin Albawani, Raden Sholihuddin, Muhammad Thoriq Siagian, Pangestu Sandya Etniko Singgih Putra Pratama Singgih Putra Pratama Siregar, Talitha Aurora Nadenggan Sri Kuswayati Subairi Subairi Sugiarto Sukandar, Ivan Christopher Sulthan Ahmad Sunarko, Victor Immanuel Supangkat, Dwiki Aditama Suryaningsih, Evi Suwito Suwito Syahrul Munir Syahrul Munir Syaifulloh, Dafauzan Bilal Tarsinah Sumarni Taufiqqurrahman, Husain Taufiqurrahman, Rahmadany Fahreza Thariq, Muhammad Fadli Al Titin Sumarni Trianingsih, Arini Vita Via, Yisti Wahyu S.J. Saputra Wardhani, Adil Sandy Yisti Vita Via Yisti Vita Via Yuniar Purbasari, Intan Zainal Abidin Achmad